Significant over-representation of pathway gene sets for a given gene list
Uveal Melanoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C10G3JPM
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Significant over-representation of pathway gene sets for a given gene list. Broad Institute of MIT and Harvard. doi:10.7908/C10G3JPM
Overview
Introduction

This pipeline inspects significant overlapping pathway gene sets for a given gene list using a hypergeometric test. For the gene set database, we uses GSEA MSigDB Class2: Canonical Pathways DB as a gene set data. Further details about the MsigDB gene sets, please visit The Broad Institute GSEA MsigDB

Summary

For a given gene list, a hypergeometric test was tried to find significant overlapping canonical pathways using 1320 gene sets. In terms of FDR adjusted p.values, top 5 significant overlapping gene sets are listed as below.

  • PID_ENDOTHELINPATHWAY, PID_TXA2PATHWAY, REACTOME_REGULATION_OF_INSULIN_SECRETION_BY_ACETYLCHOLINE, PID_THROMBIN_PAR4_PATHWAY, KEGG_CALCIUM_SIGNALING_PATHWAY

Results
For a given gene list, top significant overlapping canonical pathway gene sets

Table 1.  Get Full Table This table shows significant gene sets in which at least one gene is found and its FDR adjusted p.value is smaller than 0.3. the hypergeometric p-value is a probability of randomly drawing x or more successes(gene overlaps in gene set database) from the population (gene universe consisting of N number of genes) in k total draws(the number of input genes). The hypergeometric test is identical to the corresponding one-tailed version of Fisher's exact test. That is, P(X=x) = f(x| N,m,k). The FDR q.value was obtained for 1320 multiple comparison.

GS(gene set) pathway name gene.list GS size (m) n.NotInGS (n) Gene universe (N) n.drawn (k) n.found (x) p.value (p(X>=x)) FDR (q.value)
PID ENDOTHELINPATHWAY gene.list 63 45893 45956 10 3 2.926e-07 0.0001931
PID TXA2PATHWAY gene.list 57 45899 45956 10 3 2.157e-07 0.0001931
REACTOME REGULATION OF INSULIN SECRETION BY ACETYLCHOLINE gene.list 11 45945 45956 10 2 2.341e-06 0.0010300
PID THROMBIN PAR4 PATHWAY gene.list 15 45941 45956 10 2 4.468e-06 0.0014740
KEGG CALCIUM SIGNALING PATHWAY gene.list 178 45778 45956 10 3 6.720e-06 0.0015300
PID S1P META PATHWAY gene.list 21 45935 45956 10 2 8.930e-06 0.0015300
PID S1P S1P2 PATHWAY gene.list 24 45932 45956 10 2 1.173e-05 0.0015300
REACTOME GASTRIN CREB SIGNALLING PATHWAY VIA PKC AND MAPK gene.list 205 45751 45956 10 3 1.026e-05 0.0015300
REACTOME G ALPHA Q SIGNALLING EVENTS gene.list 184 45772 45956 10 3 7.422e-06 0.0015300
REACTOME ADP SIGNALLING THROUGH P2RY1 gene.list 25 45931 45956 10 2 1.275e-05 0.0015300
REACTOME THROMBOXANE SIGNALLING THROUGH TP RECEPTOR gene.list 23 45933 45956 10 2 1.076e-05 0.0015300
PID S1P S1P3 PATHWAY gene.list 29 45927 45956 10 2 1.725e-05 0.0018970
REACTOME SIGNAL AMPLIFICATION gene.list 31 45925 45956 10 2 1.975e-05 0.0019860
REACTOME THROMBIN SIGNALLING THROUGH PROTEINASE ACTIVATED RECEPTORS PARS gene.list 32 45924 45956 10 2 2.106e-05 0.0019860
ST ADRENERGIC gene.list 36 45920 45956 10 2 2.674e-05 0.0022060
PID ARF6 PATHWAY gene.list 35 45921 45956 10 2 2.526e-05 0.0022060
PID ER NONGENOMIC PATHWAY gene.list 41 45915 45956 10 2 3.479e-05 0.0027010
PID THROMBIN PAR1 PATHWAY gene.list 43 45913 45956 10 2 3.830e-05 0.0028090
PID LYSOPHOSPHOLIPID PATHWAY gene.list 66 45890 45956 10 2 9.073e-05 0.0063040
KEGG LONG TERM DEPRESSION gene.list 70 45886 45956 10 2 1.021e-04 0.0067390

Figure 1.  Get High-res Image This figure is an event heatmap indicating gene matches across gene sets

Methods & Data
Input

  • Gene set database = c2.cp.v4.0.symbols.gmt

Hypergeometric Test

For a given gene list, it uses a hypergeometric test to get a significance of each overlapping pathway gene set. The hypergeometric p-value is obtained by R library function phyper() and is defined as a probability of randomly drawing x or more successes(gene matches) from the population consisting N genes in k(the input genes) total draws.

  • a cumulative p-value using the R function phyper():

    • ex). a probability to see at least x genes in the group is defined as p(X>=x) = 1 - p(X<=x)= 1 - phyper(x-1, m, n, k, lower.tail=FALSE, log.p=FALSE) that is, f(x| N, m, k) = (m) C (k) * ((N-m) C (n-k)) / ((N) C (n))

  • The hypergeometric test is identical to the corresponding one-tailed version of Fisher's exact test.

    • ex). Fisher' exact test = matrix(c(n.Found, n.GS-n.Found, n.drawn-n.Found, n.NotGS- (n.drawn-n.Found)), nrow=2, dimnames = list(inputGenes = c("Found", "NotFound"),GeneUniverse = c("GS", "nonGS")) )

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Johnson, N.L., et al, Univariate Discrete Distributions, Second Edition, Wiley (1992)
[2] Berkopec, Aleš, HyperQuick algorithm for discrete hypergeometric distribution, Journal of Discrete Algorithms:341-347 (2007)
[3] Tamayo, et al, Molecular Signatures Database, MSigDB, PNAS:15545-15550 (2005)
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